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arXiv Computer Vision By Wenya Su, Kai Luo, Di Wen, Ruiping Liu, Yufan Chen, Junwei Zheng, Kunyu Peng, Kailun Yang

FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapters

Read the original on arXiv Computer Vision →

FUSEye is a lightweight training framework that adapts a frozen COCO‑pretrained YOLO26‑x detector for fisheye images by adding only about 227k parameters. It introduces three modules: GridViews to enlarge compressed boundary regions, Z‑Adapters to correct distortion‑induced feature misalignment, and AgreeFusion to fuse detections across overlapping views. On the WoodScape fisheye benchmark, FUSEye boosts YOLO26‑x mAP50 from 0.148 to 0.266 while requiring only 25% of the labeled data to achieve 0.2597 mAP50, and it also improves YOLOv8‑11 detectors.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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